AlKhawarizmi Engineering Journal最新文献_第9页

Influential Factors on Aerosol Change During COVID-19 in Ayutthaya, Thailand 泰国大城府新冠肺炎期间气溶胶变化的影响因素

AlKhawarizmi Engineering Journal Pub Date : 2021-08-31 DOI: 10.4186/ej.2021.25.8.187

A. Kodaka, N. Leelawat, Yasushi Onda, Jing Tang, A. Laosunthara, Kumpol Saengtabtim, Piyaporn Sochoeiya, N. Kohtake

{"title":"Influential Factors on Aerosol Change During COVID-19 in Ayutthaya, Thailand","authors":"A. Kodaka, N. Leelawat, Yasushi Onda, Jing Tang, A. Laosunthara, Kumpol Saengtabtim, Piyaporn Sochoeiya, N. Kohtake","doi":"10.4186/ej.2021.25.8.187","DOIUrl":"https://doi.org/10.4186/ej.2021.25.8.187","url":null,"abstract":"Various interventions were made by the Thai government to prevent the COVID-19 spread by controlling socioeconomic activities, but the effectiveness of these interventions and other factors have not yet been fully clarified. Thus, this study aims to provide further scientific evidence on those potential factors which affect the socioeconomic activities changes during the pandemic, by using spatial analysis on atmospheric composition. By taking Phra Nakhon Si Ayutthaya Province, Thailand as the case area. Results of the government's COVID-19 measures and statuses of industries were compared with changes in aerosols, including PM 2.5 which was analyzed by Google Earth Engine with nine open datasets including meteorological and hydrological factors. The analysis revealed that the aerosol index in ueban area of the province decreased at 28.03% in 2020 compared in 2019. Besides, PM 2.5 drastically decreased from March 2020, even without the influence of wind speed which as the highest causal relationship, and kept low level compared with previous years. The reason of the tendency would be explained that other than government interventions including national-level state of the emergency decree, reduction of factories' activities at Rojana Industrial Park and reduction of the number of tourists had significant influence to reduce the mean value of PM 2.5.","PeriodicalId":32885,"journal":{"name":"AlKhawarizmi Engineering Journal","volume":"83 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74184182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Life Cycle Carbon Dioxide Emissions Assessment in the Design Phase: A Case of a Green Building in Vietnam 设计阶段生命周期二氧化碳排放评估:以越南绿色建筑为例

AlKhawarizmi Engineering Journal Pub Date : 2021-07-31 DOI: 10.4186/ej.2021.25.7.121

Dinh-Linh Le, The-Quan Nguyen, Khoa Do Huu

引用次数: 1

Machine Learning Models for Inferring the Axial Strength in Short Concrete-Filled Steel Tube Columns Infilled with Various Strength Concrete 不同强度混凝土短钢管混凝土柱轴向强度推断的机器学习模型

AlKhawarizmi Engineering Journal Pub Date : 2021-07-31 DOI: 10.4186/ej.2021.25.7.135

Ngoc-Tri Ngo, H. Le, V. Huynh, Thi-Phuong-Trang Pham

引用次数: 3

A Fuzzy Credibility-Based Chance-Constrained Optimization Model for Multiple-Objective Aggregate Production Planning in a Supply Chain under an Uncertain Environment 不确定环境下供应链多目标总生产计划的模糊可信机会约束优化模型

AlKhawarizmi Engineering Journal Pub Date : 2021-07-31 DOI: 10.4186/ej.2021.25.7.31

Doan Hoang Tuan, N. Chiadamrong

引用次数: 2

Support Vector Machine for Regression of Ultimate Strength of Trusses: A Comparative Study 桁架极限强度回归的支持向量机比较研究

AlKhawarizmi Engineering Journal Pub Date : 2021-07-31 DOI: 10.4186/ej.2021.25.7.157

V. Truong, H. Pham

{"title":"Support Vector Machine for Regression of Ultimate Strength of Trusses: A Comparative Study","authors":"V. Truong, H. Pham","doi":"10.4186/ej.2021.25.7.157","DOIUrl":"https://doi.org/10.4186/ej.2021.25.7.157","url":null,"abstract":". Thanks to the rapid development of computer science, direct analyses have been increasingly used in the design of structures in lieu of member-based design methods using the effective length factor. In a direct analysis, the ultimate strength of a whole structure can be sufficiently estimated, so that the need for member capacity checks is eliminated. However, in complicated structural design problems where many structural analyses are required, the use of direct analyses requires an excessive computation cost. In such cases, Machine Learning (ML) algorithms are used to build metamodels that can predict the structural responses without performing costly structural analysis. In this paper, the support vector machine (SVM) algorithm is employed for the first time to develop a metamodel for predicting the ultimate strength of trusses using direct analysis. Several kernel functions for the SVM model, including linear, sigmoid, polynomial, radial basis function (RBF), are considered. A planar 39-bar nonlinear inelastic steel truss is taken to study the performance of the kernel functions. The results confirm the applicability of the SVM-based metamodel for predicting the ultimate strength of trusses. In particular, the RBF appears to be the best kernel among others. This investigation also provides a deeper understanding of the effect of the parameters on the efficiency of the kernel functions.","PeriodicalId":32885,"journal":{"name":"AlKhawarizmi Engineering Journal","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80133319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Coastal Upwelling Investigation in the Gulf of Thailand Using Ekman Transport and Sea Surface Temperature Upwelling Indices 利用Ekman输运和海温上升流指数研究泰国湾沿岸上升流

AlKhawarizmi Engineering Journal Pub Date : 2021-07-31 DOI: 10.4186/ej.2021.25.7.1

Pacharamon Sripoonpan, Suriyan Saramul

引用次数: 2

EmoCNN: Encoding Emotional Expression from Text to Word Vector and Classifying Emotions—A Case Study in Thai Social Network Conversation EmoCNN:从文本到词向量的情感表达编码和情感分类——以泰国社交网络会话为例

AlKhawarizmi Engineering Journal Pub Date : 2021-07-31 DOI: 10.4186/ej.2021.25.7.73

K. Wongpatikaseree, Y. Kaewpitakkun, Sumeth Yuenyong, Siriwon Matsuo, P. Yomaboot

引用次数: 1

Enhancing BIM Diffusion through Pilot Projects in Vietnam 通过越南试点项目加强BIM的推广

AlKhawarizmi Engineering Journal Pub Date : 2021-07-31 DOI: 10.4186/ej.2021.25.7.167

Quynh To Thi Huong, E. Lou, Nam Le Thi Hoai

引用次数: 3

A Case Study of BIM Application in a Public Construction Project Management Unit in Vietnam: Lessons Learned and Organizational Changes BIM在越南某公共建设项目管理单位的应用案例研究:经验教训与组织变革

AlKhawarizmi Engineering Journal Pub Date : 2021-07-31 DOI: 10.4186/ej.2021.25.7.177

The-Quan Nguyen, Quoc Viet Dao

引用次数: 11

Propagation of Own Non- Axisymmetric Waves in Viscoelastic Three-Layered Cylindrical Shells 非轴对称波在粘弹性三层圆柱壳中的传播

AlKhawarizmi Engineering Journal Pub Date : 2021-07-31 DOI: 10.4186/ej.2021.25.7.97

I. I. Safarov, M. Teshaev, A. Marasulov, B. Nuriddinov

引用次数: 6